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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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Lee D, Seo J, Jeong HC, Lee H, Lee SB. The Perspectives of Early Diagnosis of Schizophrenia Through the Detection of Epigenomics-Based Biomarkers in iPSC-Derived Neurons. Front Mol Neurosci 2021; 14:756613. [PMID: 34867186 PMCID: PMC8633873 DOI: 10.3389/fnmol.2021.756613] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/20/2021] [Indexed: 12/11/2022] Open
Abstract
The lack of early diagnostic biomarkers for schizophrenia greatly limits treatment options that deliver therapeutic agents to affected cells at a timely manner. While previous schizophrenia biomarker research has identified various biological signals that are correlated with certain diseases, their reliability and practicality as an early diagnostic tool remains unclear. In this article, we discuss the use of atypical epigenetic and/or consequent transcriptional alterations (ETAs) as biomarkers of early-stage schizophrenia. Furthermore, we review the viability of discovering and applying these biomarkers through the use of cutting-edge technologies such as human induced pluripotent stem cell (iPSC)-derived neurons, brain models, and single-cell level analyses.
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Affiliation(s)
- Davin Lee
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jinsoo Seo
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Hae Chan Jeong
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Hyosang Lee
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Sung Bae Lee
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
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Wagh VV, Vyas P, Agrawal S, Pachpor TA, Paralikar V, Khare SP. Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review. Front Genet 2021; 12:736483. [PMID: 34721526 PMCID: PMC8548640 DOI: 10.3389/fgene.2021.736483] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/31/2021] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.
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Affiliation(s)
- Vipul Vilas Wagh
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Parin Vyas
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
| | - Suchita Agrawal
- The Psychiatry Unit, KEM Hospital and KEM Hospital Research Centre, Pune, India
| | | | - Vasudeo Paralikar
- The Psychiatry Unit, KEM Hospital and KEM Hospital Research Centre, Pune, India
| | - Satyajeet P Khare
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, India
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Koola MM. Alpha7 nicotinic-N-methyl-D-aspartate hypothesis in the treatment of schizophrenia and beyond. Hum Psychopharmacol 2021; 36:1-16. [PMID: 32965756 DOI: 10.1002/hup.2758] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 12/12/2022]
Abstract
Development of novel treatments for positive, cognitive, and negative symptoms continue to be a high-priority area of schizophrenia research and a major unmet clinical need. Given that all randomized controlled trials (RCTs) conducted to date failed with one add-on medication/mechanism of action, future RCTs with the same approach are not warranted. Even if the field develops a medication for cognition, others are still needed to treat negative and positive symptoms. Therefore, fixing one domain does not completely solve the problem. Also, targeting the cholinergic system, glutamatergic system, and cholinergic plus alpha7 nicotinic and N-methyl-D-aspartate (NMDA) receptors failed independently. Hence, targeting other less important pathophysiological mechanisms/targets is unlikely to be successful. Meta-analyses of RCTs targeting major pathophysiological mechanisms have found some efficacy signal in schizophrenia; thus, combination treatments with different mechanisms of action may enhance the efficacy signal. The objective of this article is to highlight the importance of conducting RCTs with novel combination treatments in schizophrenia to develop antischizophrenia treatments. Positive RCTs with novel combination treatments that target the alpha7 nicotinic and NMDA receptors simultaneously may lead to a disease-modifying therapeutic armamentarium in schizophrenia. Novel combination treatments that concurrently improve the three domains of psychopathology and several prognostic and theranostic biomarkers may facilitate therapeutic discovery in schizophrenia.
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Affiliation(s)
- Maju Mathew Koola
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, New York, USA
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Li C, Shi Z, Ji J, Niu G, Liu Z. Associations of C-Reactive Protein, Free Triiodothyronine, Thyroid Stimulating Hormone and Creatinine Levels with Agitation in Patients with Schizophrenia: A Comparative Cross-Sectional Study. Neuropsychiatr Dis Treat 2021; 17:2575-2585. [PMID: 34408419 PMCID: PMC8364367 DOI: 10.2147/ndt.s322005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/01/2021] [Indexed: 01/11/2023] Open
Abstract
PURPOSE Agitation is prevalent among inpatients with schizophrenia. The aim of this study was to investigate whether biochemical parameters are associated with agitation in schizophrenia. PATIENTS AND METHODS Agitation was evaluated by the Positive and Negative Syndrome Scale-Excited Component questionnaire (PANSS-EC). Fasting serum levels of C-reactive protein (CRP), free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), uric acid (UA), creatinine, glucose and lipids were measured. RESULTS The analysis included 154 inpatients with schizophrenia (71 with agitation, 83 without agitation) and 75 healthy control subjects. Patients with schizophrenia and agitation had higher serum levels of CRP, FT3, FT4 and UA as well as lower levels of serum TSH and creatinine than patients without agitation (all P < 0.05). Multivariate logistic regression analysis indicated that serum CRP (odds ratio [OR] = 1.470, P = 0.001), FT3 (OR = 13.026, P < 0.001), TSH (OR = 0.758, P = 0.033) and creatinine (OR = 0.965, P = 0.004) were significantly associated with agitation in schizophrenia. CRP, FT3, TSH and creatinine achieved an area under the ROC curve of 0.626, 0.728, 0.620 and 0.663 respectively in discriminating schizophrenia with or without agitation. CONCLUSION Increased serum CRP and FT3 levels and decreased serum TSH and creatinine levels are independent risk factors for agitation in hospitalized patients with schizophrenia. Inflammation, thyroid hormones and renal function may be involved in the pathogenesis of agitation in schizophrenia.
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Affiliation(s)
- Chao Li
- Department of Psychiatry, Jining Medical University, Jining, 272067, People's Republic of China
| | - Zhenchun Shi
- Department of Psychiatry, Shandong Mental Health Center, Jinan, 250014, People's Republic of China
| | - Jiacui Ji
- Department of Psychiatry, Shandong Mental Health Center, Jinan, 250014, People's Republic of China
| | - Gengyun Niu
- Department of Psychiatry, Jining Medical University, Jining, 272067, People's Republic of China
| | - Zengxun Liu
- Department of Psychiatry, Shandong Mental Health Center, Jinan, 250014, People's Republic of China
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6
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Kang M, Kim E, Chen S, Bentley WE, Kelly DL, Payne GF. Signal processing approach to probe chemical space for discriminating redox signatures. Biosens Bioelectron 2018; 112:127-135. [DOI: 10.1016/j.bios.2018.04.032] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 04/03/2018] [Accepted: 04/16/2018] [Indexed: 12/22/2022]
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Goldsmith DR, Crooks CL, Walker EF, Cotes RO. An Update on Promising Biomarkers in Schizophrenia. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2018; 16:153-163. [PMID: 31975910 DOI: 10.1176/appi.focus.20170046] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Given the heterogeneity of symptoms in patients with schizophrenia and current treatment limitations, biomarkers may play an important role in diagnosis, subtype stratification, and the assessment of treatment response. Though many potential biomarkers have been studied, we have chosen to focus on some of the most promising and potentially clinically relevant biomarkers to review herein. These include markers of inflammation, neuroimaging biomarkers, brain-derived neurotrophic factor, genetic/epigenetic markers, and speech analysis. This will provide a broad overview of putative biomarkers that could become clinically relevant in the future, though none currently appear ready to assist the clinician in identifying cases of schizophrenia, subtypes of the disorder, treatment choice, or response. Nonetheless, some biomarkers, such as C-reactive protein (CRP), may be useful at identifying individuals who may be more highly inflamed, which could drive treatment choice. Though checking CRP is not a standard of practice, this is one example of how biomarkers may drive treatment decisions in the future, supporting precision medicine. Similarly, technological advances may one day allow clinicians to detect changes in speech patterns, which could represent a noninvasive, clinically useful tool in the future. We conclude the review by highlighting two important potential clinical uses for biomarkers in schizophrenia: the identification of individuals who may convert from clinical high risk and the stratification of patients via different biomarkers that may supersede clinical diagnosis. Given the enormous burden of illness of schizophrenia, the search for clinically relevant biomarkers is of great importance to improve the lives of patients with the disorder.
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Affiliation(s)
- David R Goldsmith
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Courtney L Crooks
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Elaine F Walker
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
| | - Robert O Cotes
- Dr. Goldsmith, Dr. Crooks, and Dr. Cotes are with the Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. Dr. Crooks is also with the Electronic Systems Laboratory, Georgia Tech Research Institute, Atlanta. Dr. Walker is with the Department of Psychology, Emory University
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Paulus MP, Huys QJM, Maia TV. A Roadmap for the Development of Applied Computational Psychiatry. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:386-392. [PMID: 28018986 DOI: 10.1016/j.bpsc.2016.05.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. METHODS Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational-psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. RESULTS Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. CONCLUSIONS A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.
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Affiliation(s)
- Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK; Psychiatry, University of California San Diego, La Jolla, CA
| | - Quentin J M Huys
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Switzerland; Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Switzerland
| | - Tiago V Maia
- Institute for Molecular Medicine, School of Medicine, University of Lisbon, Portugal
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Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset. Transl Psychiatry 2015; 5:e601. [PMID: 26171982 PMCID: PMC5068725 DOI: 10.1038/tp.2015.91] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 05/21/2015] [Indexed: 12/31/2022] Open
Abstract
Recent research efforts have progressively shifted towards preventative psychiatry and prognostic identification of individuals before disease onset. We describe the development of a serum biomarker test for the identification of individuals at risk of developing schizophrenia based on multiplex immunoassay profiling analysis of 957 serum samples. First, we conducted a meta-analysis of five independent cohorts of 127 first-onset drug-naive schizophrenia patients and 204 controls. Using least absolute shrinkage and selection operator regression, we identified an optimal panel of 26 biomarkers that best discriminated patients and controls. Next, we successfully validated this biomarker panel using two independent validation cohorts of 93 patients and 88 controls, which yielded an area under the curve (AUC) of 0.97 (0.95-1.00) for schizophrenia detection. Finally, we tested its predictive performance for identifying patients before onset of psychosis using two cohorts of 445 pre-onset or at-risk individuals. The predictive performance achieved by the panel was excellent for identifying USA military personnel (AUC: 0.90 (0.86-0.95)) and help-seeking prodromal individuals (AUC: 0.82 (0.71-0.93)) who developed schizophrenia up to 2 years after baseline sampling. The performance increased further using the latter cohort following the incorporation of CAARMS (Comprehensive Assessment of At-Risk Mental State) positive subscale symptom scores into the model (AUC: 0.90 (0.82-0.98)). The current findings may represent the first successful step towards a test that could address the clinical need for early intervention in psychiatry. Further developments of a combined molecular/symptom-based test will aid clinicians in the identification of vulnerable patients early in the disease process, allowing more effective therapeutic intervention before overt disease onset.
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English JA, Wynne K, Cagney G, Cotter DR. Targeted proteomics for validation of biomarkers in early psychosis. Biol Psychiatry 2014; 76:e7-9. [PMID: 24332930 DOI: 10.1016/j.biopsych.2013.11.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 11/06/2013] [Indexed: 11/29/2022]
Affiliation(s)
- Jane A English
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland.
| | - Kieran Wynne
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Gerard Cagney
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - David R Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, ERC Beaumont Hospital, Dublin, Ireland
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11
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Applications of blood-based protein biomarker strategies in the study of psychiatric disorders. Prog Neurobiol 2014; 122:45-72. [PMID: 25173695 DOI: 10.1016/j.pneurobio.2014.08.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 08/11/2014] [Accepted: 08/19/2014] [Indexed: 02/07/2023]
Abstract
Major psychiatric disorders such as schizophrenia, major depressive and bipolar disorders are severe, chronic and debilitating, and are associated with high disease burden and healthcare costs. Currently, diagnoses of these disorders rely on interview-based assessments of subjective self-reported symptoms. Early diagnosis is difficult, misdiagnosis is a frequent occurrence and there are no objective tests that aid in the prediction of individual responses to treatment. Consequently, validated biomarkers are urgently needed to help address these unmet clinical needs. Historically, psychiatric disorders are viewed as brain disorders and consequently only a few researchers have as yet evaluated systemic changes in psychiatric patients. However, promising research has begun to challenge this concept and there is an increasing awareness that disease-related changes can be traced in the peripheral system which may even be involved in the precipitation of disease onset and course. Converging evidence from molecular profiling analysis of blood serum/plasma have revealed robust molecular changes in psychiatric patients, suggesting that these disorders may be detectable in other systems of the body such as the circulating blood. In this review, we discuss the current clinical needs in psychiatry, highlight the importance of biomarkers in the field, and review a representative selection of biomarker studies to highlight opportunities for the implementation of personalized medicine approaches in the field of psychiatry. It is anticipated that the implementation of validated biomarker tests will not only improve the diagnosis and more effective treatment of psychiatric patients, but also improve prognosis and disease outcome.
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Biomarkers in schizophrenia: a brief conceptual consideration. DISEASE MARKERS 2013; 35:3-9. [PMID: 24167344 PMCID: PMC3774970 DOI: 10.1155/2013/510402] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/01/2013] [Accepted: 04/16/2013] [Indexed: 12/23/2022]
Abstract
Biomarkers have been sought after in the field of schizophrenia research for decades. In this paper, we discuss some of the concepts around developing biomarkers in an effort to understand why the use of biomarkers for schizophrenia has not been realized. In particular, we address the following 4 questions. Why would we need a diagnostic biomarker for schizophrenia? How is a biomarker typically defined and how does that influence the discovery of biomarkers in schizophrenia? What is the best use of biomarkers in schizophrenia? Do any biomarkers for schizophrenia currently exist? Thus, while we suggest that no biomarker currently exists for schizophrenia, the heterogeneity associated with schizophrenia will most likely need to be taken into account which will result in multiple biomarkers that identify the multiple underlying pathophysiological processes involved in schizophrenia. Therefore, much additional work will be required prior to obtaining any well-established biomarkers for schizophrenia.
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Tomasik J, Schwarz E, Guest PC, Bahn S. Blood test for schizophrenia. Eur Arch Psychiatry Clin Neurosci 2012; 262 Suppl 2:S79-83. [PMID: 22923188 DOI: 10.1007/s00406-012-0354-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2012] [Accepted: 08/10/2012] [Indexed: 12/26/2022]
Abstract
Schizophrenia is a complex disease with mostly unknown aetiology. Rapid development of molecular profiling technologies in recent years has facilitated identification of physiological processes associated with schizophrenia. In particular, changes have been found in the blood of schizophrenia patients, and this offers an accessible and efficient alternative to brain samples for research purposes. Here, we review the metabolic, immune and hormonal imbalances characterised in subgroups of schizophrenia patients and discuss potential applications in differential diagnosis, prognosis and early intervention. We also describe development of the first validated biological blood test for diagnosis of schizophrenia, and the challenges involved after introduction of this into clinical practice. Moreover, we discuss possibilities for further research on biomarkers for diagnostic applications in schizophrenia. Promising research avenues include extension to functional analysis of blood cells and applications in prediction of drug response and novel drug discovery.
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Affiliation(s)
- Jakub Tomasik
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
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Abstract
AbstractThere is an urgent necessity of designing translational strategies to schizophrenia, a mental disorder that affects 30 million people worldwide. Proteomic studies have been providing data enough to pave the way for that, but these need to be connected in a concise manner in order to translate laboratorial findings to real improvements in the lives of the patients.
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15
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Abstract
AbstractThere is an urgent necessity of designing translational strategies to schizophrenia, a mental disorder that affects 30 million people worldwide. Proteomic studies have been providing data enough to pave the way for that, but these need to be connected in a concise manner in order to translate laboratorial findings to real improvements in the lives of the patients.
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